Active-set newton algorithm for overcomplete non-negative representations of audio

IEEE Transactions on Audio, Speech and Language Processing

Volumn 21, Issue 11, 2013, Pages 2277-2289

  Virtanen, Tuomas ^a   Gemmeke, Jort Florent ^b   Raj, Bhiksha ^c  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c Carnegie Mellon University   (United States)

Author keywords

Acoustic signal analysis; audio source separation; convex optimization; Newton algorithm; non negative matrix factorization; sparse coding; sparse representation; supervised source separation

Indexed keywords

ACOUSTIC SIGNAL ANALYSIS; AUDIO SOURCE SEPARATION; NEWTON ALGORITHM; NONNEGATIVE MATRIX FACTORIZATION; SPARSE CODING; SPARSE REPRESENTATION; SUPERVISED SOURCE SEPARATIONS;

ATOMS; CONVEX OPTIMIZATION; NEWTON-RAPHSON METHOD; QUALITY CONTROL; SIGNAL ANALYSIS; SOURCE SEPARATION;

ALGORITHMS;

EID: 84886818613     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2013.2263144     Document Type: Article

References (50)

1. Y.-C. Cho and S. Choi, " Nonnegative features of spectro-temporal sounds for classification," Pattern Recognit. Lett., vol. 26, no. 9, pp. 1327-1336, 2005 (Pubitemid 40644695)
2. N. Bertin, R. Badeau, and E. Vincent, " Enforcing harmonicity and smoothness in Bayesian non-negative matrix factorization applied to polyphonic music transcription,"IEEE Trans. Audio, Speech, Lang. Process., vol. 18, no. 3, pp. 538-549, Mar. 2010
3. J. Gemmeke, T. Virtanen, and A. Hurmalainen, " Exemplar-based sparse representations for noise robust automatic speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 7, pp. 2067-2080, Sep. 2011
4. T. Virtanen, "Monaural sound source separation by non-negative matrix factorization with temporal continuity and sparseness criteria," IEEE Trans. Audio, Speech, Language Process., vol. 15, no. 3, pp. 1066-1074, Mar. 2007
5. B. Raj, T. Virtanen, S. Chaudhuri, and R. Singh, " Non-negative matrix factorization based compensation of music for automatic speech recognition," in Proc. Interspeech ?10, Tokyo, Jap an, 2010
6. D. Bansal, B. Raj, and P. Smaragdis, " Bandwidth expansion of narrowband speech using non-negative matrix factorization," in Proc. 9th Eur. Conf. Speech Commun. Technol., Lisbon, P ortugal, 2003
7. A. Ozerov, "Multichannel nonnegativematrix factorization in convolutive mixtures for audio source separation," IEEE Trans. Audio, Speech, Lang. Process., vol. 18, no. 3 , pp. 550-563, Mar. 2010
8. M. D. Plumbley, T. Blumensath, L. Daudet, R. Gribonval, and M. E. Davies, "Sparse representations in audio & music: From coding to source separation," Proc. IEEE, vol. 98, no. 6, pp. 995-1005, Jun. 2009
9. J. Nikunen and T. Virtanen, " Object-based audio coding using nonnegative matrix factorization for the spectrogram representation," in Proc. 128th Audio Eng. Soc. Conv., London, U.K., 2010
10. ] D. D. Lee and H. S. Seung, " Algorithms for non-negativematrix factorization," in Proc. Neural Inf. Process. Syst., Denver, CO, USA, 2000, pp. 556-562
11. A. Cichocki, R. Zdunek, A. H. Phan, and S. Amari, Nonnegative Matrix and Tensor Factorizations. New York, NY, USA: Wiley, 2009
12. S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, "An interior-point method for large-scale-regularized least squares," IEEE J. Sel. Topics Signal Process., vol. 1, no. 4, pp. 606-617, Dec. 2007 (Pubitemid 351266626)
13. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression," Ann. Statist., vol. 32, no. 2, pp. 407-499, 2004 (Pubitemid 41250302)
14. J. F. Murray and K. Kreutz-Delgado, " Sparse image coding using learned overcomplete dictionaries," in Proc. IEEE Workshop Mach. Learn. Signal Process., Sao Luis, Brazil, 2004, pp. 579-588 (Pubitemid 40557224)
15. P. Smaragdis and J. C. Brown, " Non-negative matrix factorization for polyphonic music transcription," in Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust., New Paltz, NY, USA, 2003, pp. 177-180
16. ] N. B. C. Févotte and J.-L. Durrieu, " Nonnegative matrix factorization with the Itakura-Saito divergence with application to music analysis," Neural Comput., vol. 21, no. 3, 2009
17. J. Carabias-Orti, F. Rodriguez-Serrano, P. Vera-Candeas, F. Canadas-Quesada, and N. Ruiz-Reyes, "Constrained non-negative sparse coding using learnt instrument templates for realtime music transcription," Eng. Applicat. Artif. Intell., 2013, to be published
18. F. Weninger and B. Schuller, " Optimization and parallelization of monaural source separation algorithms in the openBliSSART toolkit," J. Signal Process. Syst., vol. 69, no. 3, pp. 267-277, 2012
19. S. Bellavia, M. Macconi, and B. Morini, " An interior point Newtonlike method for non-negative least-squares problems with degenerate solution," Numer. Linear Algebra With Applicat., vol. 13, no. 10, pp. 825-846, 2006 (Pubitemid 44951700)
20. D. Kim, S. Sra, and I. S. Dhillon, " Fast Newton-type methods for the least squares nonnegative matrix approximation problem," in Proc. SIAM Conf. Data Mining, Minnea polis, MN, USA, 2007
21. R. Zdunek and A. Cichocki, " Nonnegative matrix factorization with constrained second-order optimization," Signal Process., vol. 87, no. 8, pp. 1904-1916, 2007 (Pubitemid 46590394)
22. C.-J. Hsieh and I. S. Dhillon, " Fast coordinate descent methods with variable selection for non-negative matrix factorization," in Proc. 17th ACMSIGKDD Int. Conf. Knowl. Discov. DataMining, San Diego, CA, USA, 2011
23. H. Van hamme, "A diagonalized Newton algorithm for non-negative sparse coding " in Proc. IEEE Int. Conf. Audio, Speech, Signal Process., Vancouver, BC, Canada, 2013, accepted for publication
24. J. Kim and H. Park, "Fast nonnegative matrix factorization: An activeset-like method and comparisons," SIAM J. Sci. Comput., vol. 33, no. 6, pp. 3261-3281, 2011
25. R. Zdunek and A. Cichocki, " Fast nonnegative matrix factorization algorithms using projected gradient approaches for large-scale problems," Comput. Intell. Neurosci., 2008, article ID 939567
26. D. L. Donoho and M. Elad, " Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization," Proc. National Acad. Sci. USA, v ol. 100, no. 5, pp. 2197-2202, 2003 (Pubitemid 36297476)
27. T. Blumensath andM. E. Davies, "Iterative hard thresholding for compressed sensing " Appl. Comput. Harmon. Anal., vol. 27, no. 3, pp. 265-274, 2009
28. ] T. Blumensath, "Compressed sensing with nonlinear observations " IEEE Trans. Inf. Theory, vol. 59, no. 6, pp. 3466-3474, Jun. 2013
29. S. Bahmani, B. Raj, and P. Boufounos, " Greedy sparsity-constrained optimization," J. Mach. Learn. Res., vol. 14, no. 3, pp. 807-841, 2013
30. S. Mallat and Z. Zhang, " Matching pursuitswith time-frequency dictionaries," IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993
31. Y. Pati, R. Rezaiifar, and P. Krishnaprasad, " Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," in Proc. Asilomar Conf. Signals, Syst. Comput., 1993
32. D. Needell and J. A. Tropp, " Cosamp: Iterative signal recovery from incomplete and inaccurate samples," Commun. ACM, vol. 53, no. 12, pp. 301-321, 2010
33. P. Smaragdis, "Extraction of speech from mixture signals," in Techniques for Noise Robustness in Automatic Speech Recognition, T.Virtanen, R. Singh, and B. Raj, Eds. New York, NY, USA:Wiley, 2012, pp. 87-108
34. M. N. Schmidt and R. K. Olsson, " Single-channel speech separation using sparse non-negative matrix factorization," in Proc. Int. Conf. Spoken Lang. Process., Pittsburgh, PA, USA, 2006
35. M. Aharon, M. Elad, and A. Bruckstein, " K-SVD and its non-negative variant for dictionary design," in Proc. SPIE Conf. Wavelet Applicat. Signal Image Process. XI, San Diego, CA, USA, 2005
36. T. Virtanen and A. T. Cemgil, " Mixtures of gamma priors for nonnegativematrix factorization based speech separation," in Proc. 8th Int. Conf. Ind. Compon. Anal. Blind Signal Separat., Paraty, Brazil, 2009
37. J. F. Gemmeke and H. Van hamme, " An hierarchical exemplar-based sparse model of speech, with an application to ASR," in Proc. IEEE Workshop Autom. Speech Recognit. Understand. (ASRU), Honolulu, HI, USA, 2011
38. P. Smaragdis, M. Shashanka, and B. Raj, " A sparse non-parametric approach for single channel separation of known sounds," in Proc. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2009
39. E. Vincent, N. Bertin, and R. Badeau, " Harmonic and inharmonic nonnegative matrix factorization for polyphonic pitch transcription," in Proc. IEEE Int. Conf. Audio, Speech, Signal Process., Las Vegas, NV, USA, 2008, pp. 109-112
40. A. T. Cemgil, "Bayesian inference for nonnegativematrix factorisation models " Comput. Intell. Neurosci., 2009, article ID 785152
41. A. Cichocki and A.-H. Phan, " Fast local algorithms for large scale nonnegative matrix and tensor factorizations," IEICE Trans. Fundam. Electron., Commun., Comput. Sci., vol. E92-A, no. 3, pp. 708-721, 2009
42. S. Boyd and L. Vanderberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004
43. P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints " J. Mach. Learn. Res., vol. 5, pp. 1457-1469, 2004
44. M. P. Cooke, J. Barker, S. P. Cunningham, and X. Shao, "An audiovisual corpus for speech perception and automatic speech recognition," J. Acoust. Soc. Amer., vol. 120, no. 5, pp. 2421-2424, 2006 (Pubitemid 44631681)
45. M. Cooke, J. R. Hershey, and S. J. Rennie, " Monaural speech separation and recognition challenge," Comput. Speech Lang., vol. 24, no. 1, pp. 1-15, 2010
46. ] D. D. Lee and H. S. Seung, " Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, pp. 788-791, Oct. 1999
47. D. Sarkar, "Methods to speed up error back-propagation learning algorithm " ACM Comput. Surv., vol. 27, no. 4, pp. 519-544, 1995
48. J. R. Hershey, S. J. Rennie, and J. Le Roux, " Factorial models for noise robust speech recognition," in Techniques for Noise Robustness in Automatic Speech Recognition, T. Virtanen, R. Singh, and B. Raj, Eds. New York, NY, USA: Wiley, 2012, pp. 311-345
49. J. O. Smith, III, "Spectral audio signal processing," Online]. Available: http://ccrma.stanford.edu/~jos/sasp/2007
50. J. F. Gemmeke and H. Van hamme, " Advances in noise robust digit recognition using hybrid exemplar-based techniques," in Proc. Interspeech, Portland, OR, USA, 2012.